The document provides an overview of a Hadoop for Developers and Admins course, including the course content which teaches how to use Apache Hadoop and write MapReduce programs, prerequisites of having basic Linux and Java familiarity, and a course outline that covers topics such as understanding distributed systems and Hadoop, running MapReduce programs, Hive, Pig, and using Hadoop in cloud computing. The course is intended for programmers, architects, and project managers who need to process large amounts of data offline. Upon completion, students will be able to use Hadoop and write MapReduce programs, understand how Hadoop supports cloud computing, and explore examples using Amazon Web Services

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HADOOPFOR DEVELOPERSANDADMIN COURSE CONTENT
You will learnhowtouse Apache Hadoopand write MapReduce programs.Youwill beginwithaquick
overviewof installingHadoop,settingitupina cluster,andthenproceedtowritingdataanalytic
programs.The course will presentthe basicconceptsof MapReduce applicationsdevelopedusing
Hadoop,includingaclose lookat frameworkcomponents,use of Hadoopfora varietyof data analysis
tasks,and numerousexamplesof Hadoopinaction.The course will furtherexaminerelated
technologiessuchasHive,Pig,andApache Accumulo.Apache Accumuloisahighlyscalable structured
store basedon Google'sBigTable,writteninJavaandoperatesoverthe HadoopDistributedFileSystem
(HDFS).Hive isdata warehouse softwareforqueryingandmanaginglarge datasets.Pigisaplatformto
take advantage of parallelizationwhenrunningdataanalysis.Finally,youwill observe how Hadoop
worksinand supportscloudcomputingandexplore exampleswithAmazonWebServicesandcase
studies.
Who ShouldAttend?
The course isintendedforprogrammers,architects,andprojectmanagerswhohave toprocesslarge
amountsof data offline.
Benefitsof Attendance:
Upon completionof thiscourse,studentswillbe able to:
• Understandbasicconceptsof MapReduce applicationsdevelopedusingHadoop
• UnderstandhowHadoopworksin and supportscloudcomputing andexplore exampleswith
AmazonWebServicesandcase studies
• Use Apache Hadoopand write MapReduce programs
Prerequisites:

2. Studentsshouldhave abasicfamiliaritywithLinuxadministrationandJava,asmost code exampleswill
be writteninJava.Familiaritywithbasicstatistical concepts(e.g.histogram, correlation) ishelpfulto
appreciate the more advanceddataprocessingexamples.
Course Outline:
What isHadoop?
UnderstandingdistributedsystemsandHadoop
ComparingSQL databasesandHadoop
UnderstandingMapReduce
CountingwordswithHadoop—runningyourfirstprogram
Historyof Hadoop
StartingHadoop
The buildingblocksof Hadoop
SettingupSSH for a Hadoopcluster
RunningHadoop
Web-basedclusterUI
Componentsof Hadoop
Workingwithfilesin HDFS
Anatomyof a MapReduce program
Readingandwriting
WritingbasicMapReduce programs
Constructingthe basictemplate of aMapReduce program
Countingthings

3. AdaptingforHadoop’sAPIchanges
StreaminginHadoop
Improvingperformance withcombiners
AdvancedMapReduce
ChainingMapReduce jobs
Joiningdatafromdifferentsources
Creatinga Bloomfilter
ProgrammingPractices
DevelopingMapReduce programs
Monitoringanddebuggingona productioncluster
Tuningfor performance
Cookbook
Passingjob-specificparameterstoyourtasks
Probingfortask-specificinformation
Partitioningintomultiple outputfiles
Inputtingfromandoutputtingtoa database
Keepingall outputinsortedorder
Managing Hadoop
Settingupparametervaluesforpractical use
Checkingsystem’shealth
Settingpermissions

4. Managing quotas
Enablingtrash
RemovingDataNodes
AddingDataNodes
Managing NameNode andSecondaryNameNode
RecoveringfromafailedNameNode
Designingnetworklayoutandrackawareness
Schedulingjobsfrommultipleusers
RunningHadoopinthe cloud
IntroducingAmazonWebServices
SettingupAWS
SettingupHadoopon EC2
RunningMapReduce programsonEC2
CleaningupandshuttingdownyourEC2 instances
AmazonElasticMapReduce andother AWSservices
ProgrammingwithPig
InstallingPig
RunningPig
LearningPigLatinthroughGrunt
SpeakingPigLatin
Workingwithuser-definedfunctions
Workingwithscripts
SeeingPiginaction—example of computingsimilarpatents

5. HadoopRelatedTechnologies
Hive
Apache Accumulo
OtherHadoop-relatedstuff
Case studies
Converting11 millionimagedocumentsfromthe New YorkTimesarchive
Miningdata at ChinaMobile
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